352-0510/02 – Optimization (OS)

Gurantor departmentDepartment of Control Systems and InstrumentationCredits6
Subject guarantorIng. Jolana Škutová, Ph.D.Subject version guarantorIng. Jolana Škutová, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Year2Semesterwinter
Study languageCzech
Year of introduction2021/2022Year of cancellation
Intended for the facultiesFSIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
SKU52 Ing. Jolana Škutová, Ph.D.
VIT60 prof. Ing. Miluše Vítečková, CSc.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 3+2
Part-time Credit and Examination 12+4

Subject aims expressed by acquired skills and competences

The main objective of the subject “Optimization” is acquainting students with methods of static and dynamic optimization. A student must be able to design of the objective function and propose the solution method. In the area of the dynamic optimization a student will be able to design so control, which ensures optimal control from the different point of view, e.g. energy, time, deviation etc.

Teaching methods

Lectures
Tutorials
Project work

Summary

Optimality criteria, conditions for optimality, constrains, forms of solution. The analytical and numerical methods of minimization of functions of single and several variables, equality constrains, inequality constrains. Kuhn-Tucker conditions, saddlepoint conditions. Minimizations of functionals, optimal control problems. Bellman’s principle of optimality and dynamic programming. Pontryagin’s minimum principle. Calculus of variations.

Compulsory literature:

RAVINDRAN, Auteur, Gintaras V. REKLAITIS and K. M. RAGSDELL. Engineering Optimization. Methods and Applications. New York: John Wilea and Sons, 1983, ISBN 0-471-05579-4. ROBERTS, Julia a Mykel KOCHENDERFER. Mathematical Optimization [online]. [cit. 2020-04-20]. Dostupné z: https://web.stanford.edu/group/sisl/k12/optimization/ SEWAK, Mohit, Md. Rezaul KARIM a Pradeep PUJARI. Practical Convolutional Neural Networks. Birmingham: Packt Publishing, 2018. ISBN 978-1-78839-230-3.

Recommended literature:

ANDERSON, Brian D. O., John B. MOORE. Optimal Control. Linear Quadratic Methods. Prentice Hal International, London, 1989, ISBN 0-13-638651-2.

Way of continuous check of knowledge in the course of semester

Credit: Passing two tests and elaboration of solutions of three programs. Exam: written part (max. 45 points)           oral part (max. 20 points)

E-learning

Other requirements

Passing two tests and elaboration of three tasks.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Optimality criteria, conditions for optimality, constrains, forms of solution. The analytical and numerical methods of minimization of functions of single and several variables, equality constrains, inequality constrains, Kuhn-Tucker conditions, saddle point conditions. Minimization of functionals, optimal control problems. Bellman’s principle of optimality and dynamic programming. Pontryagin’s minimum principle. Calculus of variations. Neural Networks - models, architecture, backpropagation learning, optimization through neural networks.

Conditions for subject completion

Conditions for completion are defined only for particular subject version and form of study

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2021/2022 (N0714A270011) Control of Machines and Processes TAŘ P Czech Ostrava 2 Compulsory study plan
2021/2022 (N0714A270011) Control of Machines and Processes TAŘ K Czech Ostrava 2 Compulsory study plan
2020/2021 (N0714A270011) Control of Machines and Processes TAŘ K Czech Ostrava 2 Compulsory study plan
2020/2021 (N0714A270011) Control of Machines and Processes TAŘ P Czech Ostrava 2 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner